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Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions
Appendix C
Observations, Consequences, and Opportunities: The Site Visits of the Committee
Table C.1, which summarizes the committee’s observations from the site visits, is structured as follows.
Column 1—Observations (what committee members saw during the site visits). Under each observation are listed one or more de-identified data points. The high-level observation is the abstraction for those data points. The committee grouped the observations into six categories:
Category 1. The medical record itself—the display, the application, the paper; in general, what the user interacts with directly.
Category 2. The health care delivery process—the workflow, what happens when, who does it, how decisions are made, how communication occurs.
Category 3. Health care professionals—what they are like, how they react to IT, and so on.
Category 4. IT infrastructure and management—the underlying computing substrate and how it is managed.
Category 5. Data capture and flow—how data are gathered, recorded, and passed among systems, records, and people.
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Category 6. Change in a sociotechnical system—how to create environments that facilitate large-scale change.
Column 2—Consequences (why the observations matter). For each observation, the committee infers one or more consequences. That is, why do we care about the observation in question? How might it affect health care delivery?
Column 3—Opportunities for Action (what we can do about the consequences). Every observation-consequence pair should provide one or more opportunities for action. Solutions known today but not yet implemented are indicated by an “S” (for short-term) in Column 3; challenges for research, where solutions are not known today, are indicated by an “R” (for research) in Column 3.
In Table C.1, the notation CxOy is used. Cx refers to Category x of the committee’s observations as grouped in the table (which lists six categories of observations), and Oy refers to a particular observation as numbered in the table (which includes a total of 25 observations).
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Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions
TABLE C.1 Committee’s Observations from Its Site Visits
Observations—What Committee Members Saw
Consequences—Why the Observations Matter
Opportunities for Action—What We Can Do About Ita
Category 1. The Medical Record Itself
1
Patient records are fragmented
Computer-based and paper records co-exist
Computer records are divided among task-specific transaction-processing systems
Users have to know where to look
Individual manually annotated work lists are the norm
Synthesis depends on intra-team conversation
Problem recognition is left to chance
Team members waste time getting information in the form they want to use
Techniques to synthesize and summarize information about the patient in and across systems with drill-downs for detail (S/R)
Mechanisms to focus on a constellation of related factors (S/R)
Single search box that returns all appropriate information in the appropriate format (R)
Alerts to problems or trends for investigation (S/R)
“Virtual patient” displays leveraging biological and disease models to reduce multiple data inputs to intelligent summaries of key human systems (R)
2
Clinical user interfaces mimic their paper predecessors
The flow sheet is the predominant display construct
No standardization of location of information or use of symbols and color
Font size is challenging
Important information and trends are easily overlooked
Cognitive burden of absorbing the information detracts from thinking about what the information means
Design reflecting human and safety factors (S)
Automatic capture and use of context (what, who, when…) (S)
Techniques to represent and capture data at multiple levels of abstraction (Care—plan, order, charting; data—raw signal, concept derived from the signal; biology) (S/R)
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Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions
Observations—What Committee Members Saw
Consequences—Why the Observations Matter
Opportunities for Action—What We Can Do About Ita
Category 1. The Medical Record Itself (continued)
3
Systems are used most often to document what has been done, frequently hours after the fact
Missed opportunity for decision or workflow support
Variable completeness and accuracy
Redundant work
See Category 5, observation 19 (C5O19)
4
Support for evidence-based medicine and computer-based advice is rare
Lost opportunity to provide patient-specific decision support
Peer-to-peer and social networking techniques for development of guidelines and decision support content (S/R)
Mass customization techniques for practice guidelines (modules) (R)
Computable knowledge structures and models (R)
Category 2. The Health Care Delivery Process
5
High complexity and coordination requirements of care
Within teams
Across teams and services within settings
Across settings
Reactive care
Handoff errors
Redundant care
Dynamically computable models to represent plan for care, workflow, escalation, and so on (R)
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Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions
Observations—What Committee Members Saw
Consequences—Why the Observations Matter
Opportunities for Action—What We Can Do About Ita
Category 2. The Health Care Delivery Process (continued)
6
Non-transparent workflow
Clinical roles and responsibilities are not explicit
Scheduling is negotiated and manual
Care processes steps and outcomes are rarely documented in machine-readable manner
No clear thinking about overall workflows, process design, and efficiency and handoff errors
Unpredictable escalation and response
Scripting languages for decision and workflow support content (S/R)
Uniform provider ID (S)
Explicit team roles and escalation paths (S/R)
Capabilities for context-aware efficient scheduling (S/R)
7
Work is frequently interrupted with gaps between steps and manual handoffs at seams of the process
See observations 5 and 6 (C2O5, C2O6)
See observations 5 and 6 (C2O5, C2O6)
8
Shift of care from inpatient, to outpatient, home, patients, families
See observations 5 and 6 (C2O5, C2O6)
See observations 5 and 6 (C2O5, C2O6)
Support for varying cultures and education (R)
9
Errors and near misses are frequent and use of data to identify patterns is rare
Low voluntary reporting that limits proactive use of near misses for system correction
Instrumented process to track steps (S/R)
Automated surveillance for potential problems (S/R)
10
Clinical research activities not well integrated into ongoing clinical care
Difficulty deciding what to charge to whom for research or care
Barriers to subject enrollment
Duplication of research and care processes
Limited learning from routine practice
Computable models of research plan, workflow, researcher roles, etc. (S/R)
Data exchange between care and research systems (S/R)
De-identification algorithms (S/R)
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Observations—What Committee Members Saw
Consequences—Why the Observations Matter
Opportunities for Action—What We Can Do About Ita
Category 3. Health Care Professionals
11
Clinical users choose speed over all else
Time is money
Each second added to the time to write each prescription in the United States adds 470 physician full-time equivalents
See Category 5, observation 19 (C5O19)
12
Clinical users do not have a consistent understanding of the purpose of a system or the functionality of the user interface
Inefficient workflow
Incomplete or inaccurate data entry
Misinterpretation of information
System work-arounds
Design system modules for use in production (operation) and simulation (training) (S)
13
Health professionals’ understanding of how IT might help is limited
Health professionals do not know what to ask for
Health professionals do not know how to test whether an IT intervention will solve their problem in their setting
Educate health professionals in systems approaches
Imbed informatics experts in clinical teams (as is done with pharmacists)
Expand informatics training programs
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Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions
Observations—What Committee Members Saw
Consequences—Why the Observations Matter
Opportunities for Action—What We Can Do About Ita
Category 4. IT Infrastructure and Management
14
Legacy systems are predominant
Each is handled as a separate implementation (set-up, profiles, management of decision support content, etc.)
Implementation focuses on the technology, not on enabling process and role changes
Management of change holds all units supported by a system to the implementation rate of the slowest member
Data flow among an organization’s systems is very limited
Rigid workflow in an era of rapid change
Semantic meaning of clinical content is not explicit
Data are not easily shared within or across organizations
Clinical best practice and decision support content are not easily shared
Architectures to permit holistic management of patient information and decision support information across information systems
Decouple infrastructure, transaction processing, data aggregation, and decision/workflow support (S)
Wrap purchased applications as Web services (S)
Leverage ontology and document architectures (S)
Use open-source techniques for infrastructure layer (S)
Develop utility approaches to “operating system on demand” (mass virtualization) (S)
15
Centralization of management and reduction in the number of information systems is the predominant method for standardization
Does not support a dynamic learning health care system that can adapt to accommodate local needs and capabilities
See Category 2, observations 5 and 6 (C2O5, C2O6)
See observation 14 (C4O14)
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Observations—What Committee Members Saw
Consequences—Why the Observations Matter
Opportunities for Action—What We Can Do About Ita
Category 4. IT Infrastructure and Management
16
Implementation time lines are long and course changes are expensive
Actual implementation time lines for enterprise-wide functionality commonly exceed a decade
New systems are being implemented while the previous generations are still being rolled out
Requires investment far in advance of benefit
Inconsistent with president’s goal for electronic medical records by 2014
See observation 14 (C4O14)
17
Security and privacy compete with workflow optimization
Neither is effective
Techniques to authenticate a patient to his/her record (S/R)
Techniques to loosely couple the individual and his/her identities (S/R)
Architectures that enable confidentiality by limiting access according to need to know while supporting transparency in authorization (S/R)
18
Response times are variable (from subsecond to minutes) and long down-times occur (clinical systems down for >24 hours and equipment down for weeks)
Work-arounds
Redundant processes
Flying blind
Approaches that balance local caching of data with timeliness of data (S/R)
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Observations—What Committee Members Saw
Consequences—Why the Observations Matter
Opportunities for Action—What We Can Do About Ita
Category 5. Data Capture and Flow
19
Data capture/data entry are commonly manual
More time spent entering data than using data
Variable completeness and accuracy
Loss of opportunity for decision and workflow support
Redesign roles, process, and technology to capture data at the source as data are created (S/R)
Self-documenting sensor-rich environments (multimedia) (S/R)
See Category 1, observation 2
20
User interfaces do not reflect human factors and safety design
Improperly structured pull-down lists
Inconsistent use of location, symbol, and color
Systems intended to reduce error create new errors
Design reflecting human and safety factors (S)
21
Biomedical devices are poorly integrated in every location
Inefficient charting and intra-team conflict
Inaccurate charting (errors of omission and inappropriate copying)
Unsafe (5 rights errors)
Mechanism for positively identifying relationship of device to patient and to use (e.g., drip composition) (S)
Handle a physician’s drip order (order for substance, titration parameter), the current setting (nurse response to order), and amount actually administered (charting) as three related but separate concepts (S)
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Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions
Observations—What Committee Members Saw
Consequences—Why the Observations Matter
Opportunities for Action—What We Can Do About Ita
Category 5. Data Capture and Flow
22
Implementation of positive identification technology is problematic
Gaps in the chain of positive identification
Work-arounds are common because of missing or mismatched information
Portable devices are task-specific (different device for lab specimen and medication administration)
Unit doses of medication are not manufactured with computer-readable tags
Defeats safety objective
Limit use to subprocesses where the technology is adequate for the workflow (S)
Measure and systematically eliminate work-arounds (S)
Find better technology workflow matches (S/R)
23
Semantic interoperability is almost non-existent
Lack of interoperability limits data and knowledge reuse
Interfaces that enable entry of data in flexible ways, but that guide the user into using common fields and terminologies in a non-obtrusive fashion (S/R)
Methods to reconcile multiple references to the same real-world entities (e.g., different ways of referring to penicillin) (S/R)
Mechanisms for mining data to discover emerging patterns in data (S/R)
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Observations—What Committee Members Saw
Consequences—Why the Observations Matter
Opportunities for Action—What We Can Do About Ita
Category 6. Change in a Sociotechnical System
24
Most systems are partially or poorly or incompletely integrated into practice
Inconsistent use and work-arounds increase error
Benefits are significantly less than anticipated
Reduced investment
Focus on the desired outcomes instead of the technology (S/R)
25
Innovation requires locally adaptable systems but interoperability and evidence-based medicine require more standardization
Limited innovation and standardization
Management that encourages initiation of improvements by health professionals (S)
Technology and processes that allow local innovation and flexibility but foster collaboration and learning at a national scale (R)
aR, solutions still to be discovered (research); S, solutions known today but not implemented (short term).
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